Pratyush_Python_Visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Press '1' for population analysis of a country\n",
      "Press '2' for population graph comparision between two countries\n",
      " \n",
      "Enter Choice:1\n",
      "Enter Name of the Country:india\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    country  year  population\n",
      "696   India  1952   372000000\n",
      "697   India  1957   409000000\n",
      "698   India  1962   454000000\n",
      "699   India  1967   506000000\n",
      "700   India  1972   567000000\n",
      "701   India  1977   634000000\n",
      "702   India  1982   708000000\n",
      "703   India  1987   788000000\n",
      "704   India  1992   872000000\n",
      "705   India  1997   959000000\n",
      "706   India  2002  1034172547\n",
      "707   India  2007  1110396331\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "print(\"Press '1' for population analysis of a country\")\n",
    "print(\"Press '2' for population graph comparision between two countries\")\n",
    "print(\" \")\n",
    "choice=input(\"Enter Choice:\")\n",
    "if choice=='1':\n",
    "    name=input(\"Enter Name of the Country:\")\n",
    "    namelst=name.split(' ')\n",
    "    if len(namelst)==1:\n",
    "        place=str(namelst[0].capitalize())\n",
    "    elif len(namelst)==2:\n",
    "        place=str(namelst[0].capitalize()+' '+namelst[1].capitalize())\n",
    "        \n",
    "    #analysing and visualising\n",
    "    data=pd.read_csv(\"countries.csv\")\n",
    "    x=data[data.country==place]\n",
    "    #visual analysis\n",
    "    plt.plot(x.year,x.population/(10**6))\n",
    "    plt.xlabel(\"Year\")\n",
    "    plt.ylabel(\"Population (Millions)\")\n",
    "    plt.legend([place])\n",
    "    plt.show()\n",
    "    \n",
    "    #data\n",
    "    print(x)\n",
    "\n",
    "elif choice=='2':\n",
    "    name1=input(\"Enter Name of 1st Country:\")\n",
    "    name2=input(\"Enter Name of 2nd Country:\")\n",
    "    namelst1=name1.split(' ')\n",
    "    namelast2=name2.split(' ')\n",
    "    if len(namelst1)==1:\n",
    "        place1=str(namelst1[0].capitalize())\n",
    "    elif len(namelst1)==2:\n",
    "        place1=str(namelst1[0].capitalize()+' '+namelst1[1].capitalize())\n",
    "    \n",
    "    namelst2=name2.split(' ')\n",
    "    if len(namelst2)==1:\n",
    "        place2=str(namelst2[0].capitalize())\n",
    "    elif len(namelst2)==2:\n",
    "        place2=str(namelst2[0].capitalize()+' '+namelst2[1].capitalize())\n",
    "    \n",
    "    #analysing and visualising\n",
    "    data=pd.read_csv(\"countries.csv\")\n",
    "    a=data[data.country==place1]\n",
    "    b=data[data.country==place2]\n",
    "    #visual analysis\n",
    "    plt.plot(a.year,a.population/(10**6))\n",
    "    plt.plot(b.year,b.population/(10**6))\n",
    "    plt.xlabel(\"Year\")\n",
    "    plt.ylabel(\"Population (Millions)\")\n",
    "    plt.legend([place1,place2])\n",
    "    plt.show()\n",
    "    \n",
    "else:\n",
    "    print(\"Enter Valid Input!!\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
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}